Abstract 2170: Automated analysis of significant noncoding mutations in somatic whole cancer genomes
Authors: Felix Dietlein, Alex B. Wang, Christian Fagre, Anran Tang, Nicolle Besselink, Edwin Cuppen, Chunliang Li, Shamil R. Sunyaev, James T. Neal, Eliezer M. Van Allen
Journal: Cancer Research (2022)

Abstract Precision cancer medicine and tailoring therapies to specific molecular targets are based mainly on driver mutations in protein-coding regions of the genome. Complex computational tools were developed to characterize coding driver mutations in 30+ cancer types. However, genomic and functional characterization of noncoding drivers outside coding regions (~98% of the genome) and a systematic understanding of how these events contribute to tumor development is still lacking. While driver mutations in coding regions directly change protein functions (e.g., tonic activation of a receptor), drivers in the noncoding genome can activate genes not expressed in normal tissue, thereby recruiting them for oncogenesis. This suggests that the biology of drivers in coding and noncoding regions differs substantially, and that specific tools will be needed for the accurate interpretation of noncoding mutations in whole-genome sequencing data. Here, we developed a new statistical test that accounts for the biology of noncoding regions, including epigenetic structure, fluctuations of mutation rates, and positional clustering, for the genome-wide analysis of somatic noncoding mutations in more than 3,500 whole cancer genomes across 19 human cancer types. On average, we identified ~5 novel noncoding events per cancer type, including many of the noncoding findings from previous studies (e.g., PCAWG consortium) along with novel observations. Many significant results fell into two categories: (i) highly localized mutagenic processes not observed in the rest of the genome and (ii) significantly mutated promoter and enhancer regions of genes associated with tumor signaling. We observed that findings associated with localized processes translated into distinct transcriptional states in bulk RNA-seq data and single-cell expression profiles. Moreover, they enabled the prediction of tumor cells-of-origin. Many significantly mutated regulatory regions exhibited differential expression of their associated target gene. We validated a subset of these findings, including noncoding mutations altering the activity of the estrogen pathway in breast cancer, by performing CRISPR-interference and luciferase reporter experiments in cancer cell lines. Mutated and non-mutated samples further harbored differential epigenomic profiles, suggesting noncoding mutations directly affected the regulatory activity of their target regions. Many significantly mutated regulatory regions involved known cancer genes, while others targeted genes that had been functionally linked to cancer before but were not considered canonical cancer genes. Broadly, our work reveals that interpreting whole cancer genomes involves challenges specific to noncoding regions. Our extensive catalog of testable hypotheses provides a blueprint for prospective experimental and computational follow-up studies that build on the concepts of our work. Citation Format: Felix Dietlein, Alex B. Wang, Christian Fagre, Anran Tang, Nicolle Besselink, Edwin Cuppen, Chunliang Li, Shamil R. Sunyaev, James T. Neal, Eliezer M. Van Allen. Automated analysis of significant noncoding mutations in somatic whole cancer genomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2170.